Skin Segmentation Research Articles

Training artificial neural networks using self-organizing migrating algorithm for skin segmentation.

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.

Skin melanoma detection and classification with deep learning

Because of its rapid growth and high mortality rate, melanoma skin cancer is among the most dangerous types of skin cancer. Consequently, melanoma treatment relies heavily on early detection. Based on the U-Net architecture with VGG-16 encoder and semantic segmentation, we present a skin lesion segmentation approach for dermoscopic pictures in this study. Diagnostic imaging systems can assess the characteristics of the segmented skin lesion and assign them a classification based on those aspects. Even on computers without powerful GPUs, the training accuracy is still high enough (over 95%) using the suggested strategy, which uses fewer resources. We use the ISIC dataset, which contains dermoscopy images, to train the model in our trials. We compare the suggested skin lesion segmentation method to others that use deep learning and analyze the Sorensen-Dice and Jaccard scores to determine how well it performs. In terms of skin lesion segmentation, the experimental findings demonstrated that the proposed method outperformed the alternatives.

BMCS-Net: A Bi-directional multi-scale cascaded segmentation network based on transformer-guided feature Aggregation for medical images

convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to loss of localized details. Transformer has naturally excellent ability to model the global features and long-range correlations of the input information, which is strongly complementary to the inductive bias of CNNs. In this paper, a novel Bi-directional Multi-scale Cascaded Segmentation Network, BMCS-Net, is proposed to improve the performance of medical segmentation tasks by aggregating these features obtained from Transformers and CNNs branches. Specifically, a novel feature integration technique, termed as Two-stream Cascaded Feature Aggregation (TCFA) module, is designed to fuse features in two-stream branches, and solve the problem of gradual dilution of global information in the network. Besides, a Multi-Scale Expansion-Aware (MSEA) module based on the convolution of feature perception and expansion is introduced to capture context information, and further compensate for the loss of details. Extensive experiments demonstrated that BMCS-Net has an excellent performance on both skin and Polyp segmentation datasets.

US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept.

This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound acquisition. The system eliminates the need for external physical markers and complex training, making image fusion feasible for physicians with different experience levels. The integrated system involves a portable 3D camera for patient-specific surface acquisition, an electromagnetic tracking system, and US components. The fusion algorithm comprises two main parts: skin segmentation and rigid co-registration, both integrated into the US machine. The co-registration aligns the surface extracted from CT/MR images with the 3D surface acquired by the camera, facilitating rapid and effective fusion. Experimental tests in different settings, validate the system's accuracy, computational efficiency, noise robustness, and operator independence.

Multispectral Depolarization Based Living-Skin Detection: A New Measurement Principle.

Camera-based photoplethysmographic imaging enabled the segmentation of living-skin tissues in a video, but it has inherent limitations to be used in real-life applications such as video health monitoring and face anti-spoofing. Inspired by the use of polarization for improving vital signs monitoring (i.e. specular reflection removal), we observed that skin tissues have an attractive property of wavelength-dependent depolarization due to its multi-layer structure containing different absorbing chromophores, i.e. polarized light photons with longer wavelengths (R) have deeper skin penetrability and thus experience thorougher depolarization than those with shorter wavelengths (G and B). Thus we proposed a novel dual-polarization setup and an elegant algorithm (named "MSD") that exploits the nature of multispectral depolarization of skin tissues to detect living-skin pixels, which only requires two images sampled at the parallel and cross polarizations to estimate the characteristic chromaticity changes (R/G) caused by tissue depolarization. Our proposal was verified in both the laboratory and hospital settings (ICU and NICU) focused on anti-spoofing and patient skin segmentation. The clinical experiments in ICU also indicate the potential of MSD for skin perfusion analysis, which may lead to a new diagnostic imaging approach in the future.

Skin Cancer Segmentation using CNN

This groundbreaking research introduces a comprehensive strategy for advancing medical image segmentation, merging two pivotal concepts to significantly enhance both the accuracy and efficiency of the segmentation process. The first component of our approach involves the integration of polar transformations as a preprocessing step applied to the original dataset. This transformative technique is designed to address the challenges associated with segmenting single structures of elliptical shape in medical images, such as organs (e.g., heart and kidneys), skin lesions, polyps, and various abnormalities. By centering the polar transformation on the object's focal point, a reduction in dimensionality is achieved, coupled with a distinct separation of segmentation and localization tasks. Two distinct methodologies for selecting an optimal polar origin are proposed: one involving estimation through a segmentation neural network trained on non-polar images, and the other employing a dedicated neural network trained to pre dict the optimal origin. The second key element of our approach is around the integration of the DoubleU-Net architecture, a powerful encoder-decoder model specifically designed for the task of semantic image segmentation. DoubleU- Net is a group of two U-Net architectures, each with a specific purpose. The initial U-Net is pre-trained on VGG-19 as the encoder and uses features learned from ImageNet to provide efficient information transfer. In order to store more semantic information and content, a second U-Net was added to the base to enhance the capabilities of the network. Join Atrous Spatial Pyramid Pooling (ASPP) to develop network data extraction content. The combination of DoubleU-Net architecture and joint transformation as a step forward shows good segmentation performance in different clinical tasks, including liver segmentation, polyp detection vision, skin segmentation, and epicardial fat tissue segmentation. It shows that various medical projects, including various diagnostic methods such as colonoscopy, dermoscopy, microscopy, have a positive impact on the plan. More importantly, the method performs well in difficult cases, such as the segmentation of small and flat polyps in CVC-ClinicDB and the 2015 subset of the MICCAI Automated Polyp Detection dataset. The results demonstrate the accuracy and generality of the combination, making it the best way to evaluate medical images in context; Our study has revealed a new method of skin cancer diagnosis that combines the power of deep learning with innovation. Advanced technology. The combination of dual U-Net architecture and polar coordinate transformation not only improves the accuracy of classification of lesions but also improves the robustness of the model to changes in image features. This study contributes to the development of computer-aided diagnostic systems for early diagnosis. Experimental results show that our method provides good accuracy, sensitivity, and specificity in detecting malignant and benign tumors. Additionally, we are conducting ablation studies to determine the contribution of each presentation and treatment of skin cancer to ultimately benefit patients and determine these benefits. We also apply the transformation of the joint as the first step to improve the discrimination ability of the model. This mechanical change effectively reduces the impact caused by changes in wound size, shape, and direction by displaying the original image of the polar system. By standardizing the representation of skin diseases, polar transformation improves the model's ability to generalize across different data sets and improves overall performance.

Automatic Melanoma Skin Cancer Detection and Segmentation using Snakecut Algorithm

Early detection of melanoma skin cancer is crucial for effective treatment, and computer-aided diagnostic technologies offer promising advancements for dermatologists to make faster, more precise diagnoses of skin lesions. Segmenting skin lesions is a crucial first step towards automated Computer-Aided Diagnosis for skin cancer. This paper aims to use SnakeCut, a foreground extraction approach, to automatically segment skin lesions in HSV color space with little human interaction. Active contour (otherwise called Snake) and Improved GrabCut are the two popular methods. By decreasing the energy function of the related contour, the active contour acts as a deformable segmentation contour. Improved GrabCut uses updated iterated graph cuts to store color attributes used as segmentation signals in order to achieve foreground segmentation from close-by pixel similarities in its foreground segmentation algorithm. The proposed integrated solution, which is predicated on a probabilistic framework, is termed “SnakeCut.” We removed the outer black border using preprocessing. Later feature extraction is done using HOG and HSV and classifies the benign or melanoma state using Naïve Bayes, Decision tree, and K-nearest neighbor classifiers. The efficiency of the segmentation strategy was measured using the Jaccard Index. We compared the classification results of our method with existing state-of-the-art approaches. The study demonstrates the efficacy of Automatic SnakeCut in accurately segmenting skin lesions, thereby enhancing the performance of subsequent classification tasks. The average F-score was 0.75 on the 2017 ISIC challenge training dataset of 100 images. Compared to other methods, this study’s findings reveal that the suggested method is highly accurate.

A region of interest focused Triple UNet architecture for skin lesion segmentation

AbstractSkin lesion segmentation is a crucial step for skin lesion analysis and subsequent treatment. However, it is still a challenging task due to the irregular and fuzzy lesion borders, and the diversity of skin lesions. In this article, we propose Triple‐UNet, an organic combination of three UNet architectures with suitable modules, to automatically segment skin lesions. To enhance the target object region of the image, we design a region of interest enhancement module (ROIE) that uses the predicted score map of the first UNet. The enhanced image and the features learned by the first UNet help the second UNet obtain a better score map. Finally, the results are fine‐tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments have shown that TripleUNet achieves an accuracy of 92.5% on the ISIC‐2018 skin lesion segmentation benchmark, with Dice and mIoU of 0.909 and 0.836, respectively, which outperforms the state‐of‐the‐art algorithms.

Transient skin friction behavior associated with hemisphere-based vortex ring collisions

Global and local transient skin friction variations resulting from a large-eddy simulation study on ReΓ0=3000 vortex rings colliding with surface-mounted hemispheres of hemisphere-to-vortex-ring diameter-ratios of 0.5≤D/d≤2 are investigated here. Skin friction distribution changes due to the approaching primary vortex ring, formation of secondary/tertiary vortex rings, subsequent mutual interactions between various vortex ring structures, and azimuthal instabilities are clarified with a view to better establish their relationships when the relative hemisphere size varies. Results show that the primary vortex ring produces the highest skin friction levels when it is close to or collides with the hemisphere/flat wall. Additionally, entrainment of wavy secondary/tertiary vortex rings leads to them entangling around the primary vortex ring and resulting in segmented skin friction regions at later stages of the collision process, regardless of the diameter-ratio. Unlike smaller hemispheres, the largest D/d=2 hemisphere here restricts all skin friction changes along its surface with the flat wall unaffected. It is also revealed here that while the diameter-ratio governs the global skin friction distributions, how secondary/tertiary vortex rings interact with the primary vortex ring and how azimuthal instabilities manifest and evolve for all the various vortex rings confer strong influences on the breaking-up and dissipation of regions with high skin friction levels.

An Optimized Semantic Segmentation Framework for Human Skin Detection

The study incorporating optimization strategy in semantic segmentation is underexplored in dermatology. Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. This paper demonstrates Particle Swarm Optimization (PSO)-incorporated AlexNet framework for the skin segmentation task. The results from testing the trained model are promising. The model produced satisfactory performances even with a strict split of 50 %, confirming the high efficiency of the proposed framework. The mean Jaccard index and Dice similarity measures evaluated between the annotated and predicted mask ranged from 0.80 to 0.93 in the binary classification of pixels as “skin” versus “background”. This work identified that the location and color variability of skin pixels in the training data are crucial to obtaining a good skin segmentation performance. Further works that can be explored in this area include adopting a robust preprocessing strategy to increase data variability and improve model generalization or implementing an optimization-enhanced strategy on the existing segmentation models for comparison.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

Multi-class segmentation skin diseases using improved tuna swarm-based U-EfficientNet

Early location of melanoma, a dangerous shape of skin cancer, is basic for patients. Indeed, for master dermatologists, separating between threatening and generous melanoma could be a troublesome errand. Surgical extraction taken after early determination of melanoma is at its way to dispense with the malady that will result in passing. Extraction of generous injuries, on the other hand, will result in expanded dismalness and superfluous wellbeing care costs. Given the complexity and likeness of skin injuries, it can be troublesome to create an accurate determination. The proposed EfficientNet and UNet are combined and arrange to extend division exactness. Also, to decrease data misfortune amid the learning stage, adjusted fish swarm advancement (IMSO) is utilized to fine-tune the U-EfficientNet’s movable parameters. In this paper, a ViT-based design able to classify melanoma versus noncancerous injuries is displayed. On the HAM1000 and ISIC-2018 datasets, the proposed ViT demonstrated accomplished the normal precision of 99.78% and 10.43% FNR with computation time of 134.4632s of ISIC-2018 datasets. The proposed ViT show accomplished the normal exactness of 99.16% and 9.38% FNR in with computation time of 133.4782s of HAM1000 dataset.

A novel deep learning framework based swin transformer for dermal cancer cell classification

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer.

Human Skin Detection and Segmentation Based on Convolutional Neural Networks

Human skin detection is the process of classifying the image pixels or regions as skin or non-skin. Skin detection has many applications, such as face tracking, skin diseases, nudity recognition, hand gestures, video surveillance, web content filtering, and pornographic content filtering. Skin detection is a challenging problem due to skin color variation, the human race, aging, gender, makeup, complex backgrounds, etc. This paper suggests detecting the skin region in the image and finding the location of the skin based on a convolutional neural network. In this proposal, the proposed CNN will be modified by adding two layers before the first layer of CNN and after the last layer of CNN. The main purpose of these layers is to prepare the input image by using a sliding window, which inputs an indexed small part of the image into the CNN. The network classifies each part as skin or non-skin and then sends the result into the second suggested layer. After processing all the image pixels, the non-skin blocks are mapped to the original image as black regions. The final image contains the skin regions with black in the background. The contribution of the proposed method is the ability to detect the skin from any part of the human body, unlike previous works, which focused on one part of the body. The input image is processed as blocks instead of the entire image as in the previous works, and then in the output, the original image is reconstructed. This method works well with most of the challenges that face the detection of skin, and finally, the designed network facilitates the localization and segmentation of the skin region almost accurately, while the previous networks focused on the classification of the image as skin or non-skin. The accuracy of the detection of skin when testing with images different from the training images was 95.4%.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

PurposeThis study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).MethodsWe used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared.ResultsThe nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]).ConclusionsThe nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.

Skin Cancer Classification and Segmentation Using Deep Learning

Skin Cancer Classification and Segmentation Using Deep Learning

SG-MIAN: Self-guided multiple information aggregation network for image-level weakly supervised skin lesion segmentation

Nowadays, skin disease is becoming one of the most malignant diseases that threaten people’s health. Computer aided diagnosis based on deep learning has become a widely used technology to assist medical professionals in diagnosis, and segmentation of lesion areas is one of the most important steps in it. However, traditional medical image segmentation methods rely on numerous pixel-level labels for fully supervised training, and such labeling process is time-consuming and requires professional competence. In order to reduce the costs of pixel-level labeling, we proposed a method only using image-level label to segment skin lesion areas. Due to the lack of lesion’s spatial and intensity information in image-level labels, and the wide distribution range of irregular shape and different texture on skin lesions, the algorithm must pay great attention to the automatic lesion localization and perception of lesion boundary. In this paper, we proposed a Self-Guided Multiple Information Aggregation Network (SG-MIAN). Our backbone network MIAN utilizes the Multiple Spatial Perceptron (MSP) solely using classification information as guidance to discriminate the key classification features of lesion areas, and thereby performing more accurate localization and activation of lesion areas. Additionally, adjunct to MSP, we also proposed an Auxiliary Activation Structure (AAS) and two auxiliary loss functions to further self-guided boundary correction, achieving the goal of accurate boundary activation. To verify the effectiveness of the proposed method, we conducted extensive experiments using the HAM10000 dataset and the PH2dataset, which demonstrated superior performance compared to most existing weakly supervised segmentation methods.

A deep learning-based illumination transform for devignetting photographs of dermatological lesions

Photographs of skin lesions taken with standard digital cameras (macroscopic images) have gained wide acceptance in dermatology. However, uneven background lighting caused by nonstandard image acquisition negatively impacts lesion segmentation and diagnosis. To address this, we propose an automated illumination equalization method based on a counter exponential transform (IECET). A modified residual network (ResNet) regressor is used to automate the selection of the operational parameter of the IECET. The regressor is designed by modifying the final fully-connected layer of the baseline ResNet-50 model. The modified fully-connected layer is coupled to a regression layer in the modified ResNet regressor. A prior knowledge base is created to train the modified ResNet regressor. For this, a set of corrupted images are generated by simulating uneven background illumination on pristine images. The knowledge base is created by including pairs of value components obtained from the HSV color space version of the corrupted macroscopic images and ideal operational parameter values that maximize the peak signal-to-noise ratio (PSNR) between the pristine images and the IECET outputs. We evaluated segmentation accuracies of the deep threshold prediction network (DTP-Net), DeepLabV3+, fully convolutional network (FCN), and U-Net on the corrupted macroscopic images and output images of the IECET. The DTP-Net, DeepLabV3+, FCN, and U-Net exhibited Dice similarity coefficient (DSC) of 0.71 ± 0.26, 0.85 ± 0.15, 0.75 ± 0.22, and 0.66 ± 0.28 on corrupted images and 0.81 ± 0.17, 0.87 ± 0.12, 0.79 ± 0.18, and 0.79 ± 0.15, on the outputs of the IECET. Increase in DSC proves the ability of the IECET to improve the performance of deep learning models used to segment skin lesions on macroscopic images.